BCA in Data Analytics at BFIT Dehradun - Course, Fees, Admission, Seats, Syllabus

| Feature | Description | | | | | Duration of Course | Typically takes around 3 years to complete | | Level of Course | Designed for students | | Tuition Fees | Costs vary depending on the region and institution | | Study Options | Available as full time or part time depending on the institution | | Type of Institution | Can be either private or public depending on the specific institution | Please note; The specific details regarding the BCA in Data Analytics program at BFIT Group of Institutions such as tuition fees may differ. It is recommended to contact the institution for accurate information. The table above provides an overview based on common features of BCA, in Data Analytics programs.

According to my update in April 2023 I don't have direct access to the specific course syllabi of institutions like the BFIT Group of Institutions. Course syllabi can vary significantly from one year to another as they are regularly updated to incorporate the latest trends, technologies and academic requirements. However a Bachelor of Computer Applications (BCA) program with a focus on Data Analytics generally covers a range of topics that aim to equip students with the necessary skills for analyzing, managing and interpreting large datasets. While I cannot provide you with the syllabus for BFIT Group of Institutions I can offer you a general overview of the subjects and areas that are commonly included in a BCA program specializing in Data Analytics. This should give you an understanding of what you can expect; 1. **Introduction to Data Analytics**; This module provides an introduction to the fundamentals of data analysis, including an understanding of data types and how data analytics is crucial in todays world. 2. **Fundamentals of Programming**; In this module students will be introduced to programming languages that're relevant to data analytics such as Python or R. The focus will be on using these languages for data manipulation and analysis. 3. **Database Management Systems (DBMS)**; This module covers essential concepts related to databases along, with SQL and NoSQL databases. Students will learn how these databases function and how to efficiently manage and retrieve data from them. 4. Here are some important topics to explore when learning about data structures and algorithms; 1. Familiarize yourself with data structures like arrays lists, stacks, queues, graphs and trees. These play a role in organizing and managing data efficiently. 2. Gain an understanding of statistics for data science. This includes the basics of probability and statistics such as statistics, inferential statistics, hypothesis testing and regression analysis. They are essential for analyzing data 3. Learn about techniques and tools used in data visualization. For instance you can explore Tableau, Power BI or Python libraries like Matplotlib and Seaborn to visualize data in a way that effectively communicates your findings. 4. Dive into the world of machine learning by gaining an introduction to algorithms like supervised and unsupervised learning, neural networks, decision trees, etc. Understanding how these algorithms work is crucial for applying them to analyze data effectively. 5. Get an overview of data concepts and technologies such as Hadoop and Spark along with their ecosystems for processing large datasets efficiently. 6. Explore business intelligence by understanding how to utilize data analytics in making business decisions. Study tools and techniques used in business analytics. 7. Lastly examine the considerations surrounding privacy concerns when it comes to collecting, analyzing and utilizing data effectively. These topics will provide you with a foundation, in understanding the key aspects of data analysis. **Final Project**; A culminating assignment that usually necessitates students to put into practice the knowledge and skills they have acquired.